Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Applying Behavioral Economics To Retail
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > Applying Behavioral Economics To Retail
Business Intelligence

Applying Behavioral Economics To Retail

RamaRamakrishnan
RamaRamakrishnan
9 Min Read
SHARE

Recently, the McKinsey Quarterly published a brief article titled “A marketer’s guide to behavioral economics“. The author recommends four strategies for marketers, all inspired by research in behavioral economics.

Behavioral economics is, of course, a large and established field of academic research, complete with a Nobel Laureate (Daniel Kahneman). The academic work has been popularized in a number of books (examples: Nudge, The Winner’s Curse) over the past decade.

In my previous work at ProfitLogic/Oracle as well as my current consulting work with retailers, I have been on the lookout for opportunities to help my clients exploit these findings. Sadly, I have not come up with anything that isn’t already well-known or already being applied.

Against this backdrop, I was curious if the McKinsey article had new insights to offer; something that I could make Monday Morning useful for retailers.

More Read

Financial Trading
The Impact of Big Data and Business Intelligence on Financial Trading Market
Building Agile Processes with SOA and Business Rules
Interview – David Smith REvolution Computing
What Angry Birds Can Teach Us About Analytics
Dr. House, the computer?

Let’s take a look at the four recommendations from McKinsey.

1. Make a product’s cost less painful…

Recently, the McKinsey Quarterly published a brief article titled “A marketer’s guide to behavioral economics“. The author recommends four strategies for marketers, all inspired by research in behavioral economics.

Behavioral economics is, of course, a large and established field of academic research, complete with a Nobel Laureate (Daniel Kahneman). The academic work has been popularized in a number of books (examples: Nudge, The Winner’s Curse) over the past decade.

In my previous work at ProfitLogic/Oracle as well as my current consulting work with retailers, I have been on the lookout for opportunities to help my clients exploit these findings. Sadly, I have not come up with anything that isn’t already well-known or already being applied.

Against this backdrop, I was curious if the McKinsey article had new insights to offer; something that I could make Monday Morning useful for retailers.

Let’s take a look at the four recommendations from McKinsey.

1. Make a product’s cost less painful

In almost every purchasing decision, consumers have the option to do nothing: they can always save their money for another day. That’s why the marketer’s task is not just to beat competitors but also to persuade shoppers to part with their money in the first place.

Retailers know that allowing consumers to delay payment can dramatically increase their willingness to buy.

Even small delays in payment can soften the immediate sting of parting with your money and remove an important barrier to purchase.

Useful, yes. Novel, no. This is well-known to retailers as indicated by layaway plans, “no payments for 6 months” etc.

2. Harness the power of a default option

The evidence is overwhelming that presenting one option as a default increases the chance it will be chosen. Defaults—what you get if you don’t actively make a choice—work partly by instilling a perception of ownership before any purchase takes place, because the pleasure we derive from gains is less intense than the pain from equivalent losses.

An Italian telecom company, for example, increased the acceptance rate of an offer made to customers when they called to cancel their service. Originally, a script informed them that they would receive 100 free calls if they kept their plan. The script was reworded to say, “We have already credited your account with 100 calls—how could you use those?” Many customers did not want to give up free talk time they felt they already owned.

This is interesting and useful for some industries but I couldn’t think of a way for a retailer to act on it. If you have any ideas, do let me know.

3. Don’t overwhelm consumers with choice

When a default option isn’t possible, marketers must be wary of generating “choice overload,” which makes consumers less likely to purchase.

Large in-store assortments work against marketers in at least two ways. First, these choices make consumers work harder to find their preferred option, a potential barrier to purchase. Second, large assortments increase the likelihood that each choice will become imbued with a “negative halo”—a heightened awareness that every option requires you to forgo desirable features available in some other product. Reducing the number of options makes people likelier not only to reach a decision but also to feel more satisfied with their choice.

This is well-known and is the so-called “assortment breadth” problem.

Retailers will be the first to agree that

  • if you have too few choices, consumers won’t even notice the product
  • if you have too many, consumers may turn away from buying the product
  • there’s an optimal zone somewhere in the middle

To make it Monday Morning useful, we need to systematically quantify what the optimal number of choices is, for each product category. But this is not easy due to the number of confounding factors.

Seasonal variations in demand, inventory stockouts, store-to-store variations in demand, and the effects of price cuts and promotions all affect consumer behavior and it is very difficult to isolate the effect of just the assortment breadth on demand so that the retailer can act on it.

4. Position your preferred option carefully

Economists assume that everything has a price: your willingness to pay may be higher than mine, but each of us has a maximum price we’d be willing to pay. How marketers position a product, though, can change the equation.

… marketers sometimes benefit from offering a few clearly inferior options. Even if they don’t sell, they may increase sales of slightly better products the store really wants to move.

… many restaurants find that the second-most-expensive bottle of wine is very popular—and so is the second-cheapest. Customers who buy the former feel they are getting something special but not going over the top. Those who buy the latter feel they are getting a bargain but not being cheap. Sony found the same thing with headphones: consumers buy them at a given price if there is a more expensive option—but not if they are the most expensive option on offer.

This is not widely practiced and hence potentially useful. Making it Monday Morning useful doesn’t seem that hard either: when planning a product range, many retailers follow what’s known as a “good-better-best” pricing strategy: an entry-level, affordable item, a moderately-priced, sensible item and a high-quality, expensive item. To this, they can add an item that’s (say) 50% higher in price but only slightly better in quality/features than the current highest-price product.

After reading this section, I searched for academic work on the topic and stumbled on a real gem: a paper titled “The Effect of Product Assortment on Buyer Preferences” by Stanford Professor Itamar Simonson.

I highly recommend the paper if you can get your hands on it (unfortunately, it is behind a paywall). It is fascinating and a really fun read. Prof. Simonson describes numerous neat examples – here’s one:

Williams-Sonoma, a mail-order and retail business located in San Francisco, used to offer one home bread maker priced at $275. Later, a second home bread maker was added, which had similar features except for its larger size. The new item was priced more than 50% higher than the original bread maker. Williams-Sonoma did not sell many units of the new (relatively overpriced) item, but the sales of the less expensive bread maker almost doubled.

Overall, I got something useful from the McKinsey article and the Simonson paper. Next step: run it by my clients and see what they think. I will keep you posted.

Link to original post

TAGGED:behavioral economicsretail
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

image fx (2)
Monitoring Data Without Turning into Big Brother
Big Data Exclusive
image fx (71)
The Power of AI for Personalization in Email
Artificial Intelligence Exclusive Marketing
image fx (67)
Improving LinkedIn Ad Strategies with Data Analytics
Analytics Big Data Exclusive Software
big data and remote work
Data Helps Speech-Language Pathologists Deliver Better Results
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

AnalyticsBig DataBusiness IntelligenceData QualityExclusive

3 Ways Big Data And Business Intelligence Can Improve Your Business

6 Min Read
big data improving ecommerce industry
AnalyticsBig DataExclusive

Here’s How Big Data Analytics Has Changed the eCommerce Industry

7 Min Read
Young woman near digital screen in street at evening time
Artificial IntelligenceBig DataExclusive

Big Fashion Meets Big Data: How Fashion Industry Is Benefiting From Big Data

6 Min Read
big data helping shoppers
Big DataExclusive

Big Data Has Facilitated The Research And Development of Headphones

7 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?